In general, a Vision-aided Inertial Navigation System (VINS) fuses data from a camera and an Inertial Measurement Unit (IMU) to track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform. In this way, the VINS combines complementary sensing capabilities. For example, an IMU can accurately track dynamic motions over short time durations, while visual data can be used to estimate the pose displacement (up to scale) between consecutive views. For several reasons, VINS has gained popularity within the robotics community as a method to address GPS-denied navigation.
Among the methods employed for tracking the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform within GPS-denied environments, vision-aided inertial navigation is one of the most prominent, primarily due to its high precision and low cost. During the past decade, VINS have been successfully applied to spacecraft, automotive, and personal localization, demonstrating real-time performance.